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AI-Powered Mental Health Assessment using Emotion Detection for Real-Time Analysis

AI-Powered Mental Health Assessment using Emotion Detection ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vijay, K. Raghakeerthana, R. Milinda, Renata Rachael Thusheel, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331512965

This paper is an AI-driven mental health assessment tailored to each patient's distinct emotional needs, addressing the global mental health crisis. Using advanced machine learning and natural language processing, it aims to deliver personalized guidance for psychological and physical wellness activities, along with tools for tracking individual progress. The papers employ exploratory case studies and knowledge discovery techniques to evaluate the effectiveness of these interventions. Data mining methods reveal hidden patterns within mental health datasets, with the potential to reshape therapeutic paradigms. Expected outcomes include enhanced accessibility to mental health resources, early intervention, personalized progress monitoring, improved quality of life, and reduced stigma. By integrating sophisticated data processing, machine learning, and empathetic user interaction, it operates by redefining mental health support. The system's architecture is designed to promote both emotional and cognitive well-being, fostering positive change in mental health care delivery. © 2025 IEEE.

关键词： computer Vision conversational bot Convolution neural network Emotion detection machine learning natural language processing Real-Time Analysis

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

引用

2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Sreehari, M. Jain, Ankur Vellore Institiute of Technology Artificial intelligence and Machine Learning Department of computer science engineering specialization Bhopal India

ISBN: (纸本)9798350374711

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD);YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops. © 2024 IEEE.

关键词： Stochastic systems

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Enhancing Operational Efficiency and Security in Multi-Sectioned Businesses with Edge AI through Real-Time Analytics

Enhancing Operational Efficiency and Security in Multi-Secti...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijay, K. Jeyanth, V. Kamalesh, S. Dheva Prasath, D. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machine learning to gain a deeper understanding of customer behavior and improve operational efficiency. Traditional surveillance systems create lots of data to store and analyze, but they do not provide actionable intelligence. CCTV cameras and edge devices strategically placed throughout a facility capture real-time footage, identify visitor movement patterns, and represent critical heat zones on a heat map. This data-driven process enhances resource allocation, enhances customer experience, and enables proactive improvements in operational workflow. Edge devices enable real-time anomaly detection, such as theft or staff sitting, without compromising privacy, as the device retains all information and performs all processing locally. Our next-gen solution provides businesses with an all-inclusive monitoring system that solves today's most pressing security and operational issues, ultimately delivering better business results. © 2024 IEEE.

关键词： Resource allocation

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Smartsight: Android-Based Retinal Disease Detection using Retinal Fundus Images with XAI 8

Smartsight: Android-Based Retinal Disease Detection using Re...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Bagewadi, Manisha Reshmi, B.M. Kalligud, Nagaraj Badiger, Krutika Sonagaj, Sakshi Giraddi, Sindurani Basaveshwar Engineering College Department of Artificial Intelligence and Machine Learning Bagalkot India

ISBN: (纸本)9798331505462

Diabetic retinopathy (DR) is a serious eye condition affecting diabetic patients, stemming from microvascular damage to retinal blood vessels. If not diagnosed and treated early, DR can lead to blindness. Traditional diagnostic methods involve manual examination of retinal images by ophthalmologists, which is labor-intensive and time-consuming. This highlights the need for automated, efficient, and accurate diagnostic tools. This study proposes a deep learning-based framework using ResNet50 architecture enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for the automatic detection and classification of diabetic retinopathy from retinal images obtained via a smartphone-based fundus camera. The ResNet50 model, known for its deep residual learning capabilities, was trained on a dataset of retinal images to differentiate between healthy and DR-affected eyes. Incorporating Grad-CAM provides visual explanations by highlighting the retinal image regions most indicative of DR, thereby improving model interpretability. The proposed model demonstrated high performance, achieving 98% accuracy on the training dataset, 91% on the validation dataset, and 96% on the test dataset. These results highlight the model's robustness and potential for facilitating early DR detection, especially in resource-constrained environments with limited access to specialized ophthalmic care. © 2024 IEEE.

关键词： Diagnosis

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Insight Retina: A Deep learning Approach to Diagnose Diabetic Retinopathy

Insight Retina: A Deep Learning Approach to Diagnose Diabeti...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Akshaya, V. Sashank, S. Subhash, G.M. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350359688

The greatest cause of blindness in the world and a frequent cause of diabetes melitus is Diabetic-Retinopathy (DR). To stop vision loss in afflicted people, early identification and decision making are essential. Diabetic - Retinopathy is a eye disorder that is currently being diagnosed manually by ophthalmologists. The diagnosis and recovery takes a great deal of time, effort and money, sometimes even a risk of misdiagnosis. Deep-learning has been the most highly emerging popular method that performs better in many fields, especially in the process analysing data and as well as classifying images from medical imaging. The IdX-DR and EyeArt AI are some existing systems that are legally approved and widely used in many places. Convolution neural networks and transfer learning are widely used as they are more effective. The dataset called Asia Pacific Tele-Ophthalmology Society (APTOS) is a large collection, which contains images acquired using Fundus Imaging and is available in open-source which is used extensively. The Insight Retina uses Contrast Limited Adaptive Histogram Equalization for enhancement of images and pre-trained model Res-Net for detection, thus providing improved performance and a reliable 0.893 Kappa accuracy score from the DR model. Insight Retina then generates a report for the input image providing accurate information about the stage of the Diabetic-Retinopathy. © 2024 IEEE.

关键词： Contrastive learning

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Determining the Underlying Factors of Chronic Liver Disease through Factor Analysis 2

Determining the Underlying Factors of Chronic Liver Disease ...

引用

2nd International Conference on artificial intelligence and machine learning Applications, AIMLA 2024

作者： Balaji, V.S. Mahi, A.R. Aashif, M. Manju, M. Indira Priya, P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Chronic liver disease is a significant health concern, necessitating a comprehensive understanding of its underlying causes. The work aims to identify the key factors contributing to chronic liver disease using Factor Analysis, a statistical technique that explores the inter-relationships among a set of variables. The dataset, comprising various biochemical parameters and demographic details, is subjected to rigorous preprocessing, including imputation and standardisation. The suitability of data is determined using KMO measure value. The factors are represented by combinations of things that include the biochemical parameters protein, aminotransferase, globulin, phosphatase and *** research provides valuable insights, potentially aiding in early diagnosis and effective treatment strategies for chronic liver disease. This research underscores the utility of Factor Analysis in medical research, particularly in understanding complex diseases like chronic liver disease. The overview of Factor Analysis would include the loadings of each factor for each variable on each factor, which indicate how much each variable contributes to each factor and further help to identify which variables are most strongly associated with each underlying factor. The communalities and specific variances of the factors are *** could potentially aid in early diagnosis and effective treatment strategies for chronic liver disease. The complex relationship among various attributes of the dataset is examined thereby providing the contribution of a vital parameter to liver disease. © 2024 IEEE.

关键词： Factor analysis

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Novel Multi-Stage Multi-Task Deep learning Framework for the Cybersecurity of In-Vehicle Networks 8

Novel Multi-Stage Multi-Task Deep Learning Framework for the...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Shirole, Vaibhav Sunil Mahesh Madhnani, Harsh Bhojane, Niranjan Umakant Nandhini, K. Gite, Shilpa Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350391770

Objective - To develop a deep learning-based robust threat detection system for in-vehicle networks of autonomous vehicles to differentiate normal vehicle operation from potential *** - The Controller Area Network (CAN) network traffic dataset, including both attack and normal messages, was used for the study. Several features were present in this dataset, including class and subclass designations that help to differentiate between legitimate operational signals and possible attack messages. Feature engineering techniques were applied to the dataset. Data preparation was done after feature engineering, with the main goal being to make it ready to train the *** pre-processing the data, three distinct neural network architectures were applied utilizing a multi-task and multi-stage approach: convolutional neural network, long short-term memory, and artificial neural network. The most appropriate and useful model for the specific context was then identified by comparing the accuracies of different *** - With a multistage approach, the convolutional neural network model demonstrated remarkable training accuracies of 98.10% for Stage I (classifying attack or normal condition) and 99.08% for Stage II (classifying type of attack). Also, the Stage I and Stage II testing accuracies were 98.08% and 99.09% © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced Sound Detection and Behavior Examination for Real-Time Intruder Detection using Deep learning: A Comprehensive Security Framework 2

Advanced Sound Detection and Behavior Examination for Real-T...

引用

2nd International Conference on artificial intelligence and machine learning Applications, AIMLA 2024

作者： Harini, B. Gouri, M.P. Balaji, V.S. Sangeetha, K. Harshini, A. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The system responds to escalating security concerns with a sophisticated intruder detection system, integrating glass-breaking sound detection and behavior analysis. Using audio sensors and advanced signal processing, it prioritizes detecting glass-breaking events for scenarios involving forced entry through windows or glass doors. Behavior examination and analysis employs deep learning for real-time monitoring, alerting to suspicious activities. Immediate notifications are sent to security personnel or homeowners. Designed with privacy-preserving techniques, it adapts to diverse environments, customizable for various security needs and integrates with existing infrastructure. Representing a significant security advancement, it combines sound detection, behavior analysis, and real-time alerting, prioritizing ethical considerations and privacy preservation. This project paves the way for a safer future. © 2024 IEEE.

关键词： Face recognition

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A Deep Insight on Cricket Video to Text Summarization Using Neural Networks 10

A Deep Insight on Cricket Video to Text Summarization Using ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Gopal, J. Vijay Prasad, K. Sai Kumar, Dhatrika Kamal Prashanth, Maddhe Sai Kumar, Vuppala Praneeth Teja, I. Nikhil Sri Sai Computer Science Engineering Artificial Intelligence and Machine Learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cricket match videos are summarized. Cricket video-to-text summarization system overcomes the limitations of traditional manual summarization approach by utilizing various deep learning techniques to completely automate the summarization process, our system can extract crucial insights from lengthy cricket match footage and convert them into easily readable text formats. The system employs three-pronged approach which involves extraction of visual features using VGG16 Convolutional Neural Network (CNN), scoreboard information is extracted through Optical Character Recognition (OCR) technology, and Text Summarization performed by Long Short-Term Memory (LSTM) network. Our system revolutionizes the way cricket enthusiasts engage with match videos, providing a game-changing experience for fans worldwide. © 2024 IEEE.

关键词： Convolutional neural networks

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Drug Review: Sentiment Analysis, Using Advanced machine learning

Drug Review: Sentiment Analysis, Using Advanced Machine Lear...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Akshaya, V. Raja Ruben, B. Santhosh, K. Shafiya Banu, M. Rajalakshmi Engineering College Department Of Artificial Intelligence And Machine Learning Chennai India

ISBN: (纸本)9798350352689

'Sentiment Analysis of Drug Reviews Using machine learning Techniques,' focuses on employing advanced machine learning methods, including Bidirectional LSTM and TF-IDF, to analyze sentiments expressed in clinical drug reviews. The aim is to develop sentiment analysis model capable of classifying and describing the patient experiences with medications. the usage of Bidirectional LSTM architecture to capture contextual dependencies within sequential data, enhancing the accuracy of sentiment predictions is used. Additionally, the TF-IDF technique is employed for feature extraction, emphasizing the importance of sentiments of patients and addressing some challenges due to language. The performance results obtained during evaluation of the model contains an accuracy of 94.21%. Thus, the usage of these techniques aims to provide a comprehensive understanding of sentiment patterns in clinical drug reviews, offering valuable insights for healthcare professionals. © 2024 IEEE.

关键词： Sentiment analysis

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